Predictive analytics technology in dynamic business systems (“prediction systems”) typically provide real-time intelligence by enabling real-time decisions and recommendations to be rendered. The real-time intelligence may be instilled into any type of business process or customer interaction. These systems typically rely on business rules, data mining, statistical methods, or a combination thereof.
For example, online shopping mechanisms may rely upon prediction systems to determine the likelihood that a particular customer may purchase a particular product. By using a prediction system, online shopping mechanisms may be able to provide marketing campaigns that are targeted to particular shoppers, e.g. those shoppers that will most likely buy their products. These online shopping mechanisms and marketing tactics, generally, help to increase sales.
However, in some instances, as in the above-mentioned online shopping example, these systems may employ algorithms that involve determining a correlation between input data (e.g., products that have been purchased) and target output data (e.g., target product) to arrive at a prediction. These systems may include a prediction or recommendation engine to perform, for example, collaborative filtering and/or singular vector decomposition. Depending upon a number of factors, such as the algorithm, the handling of the data, and the manner in which the data is represented or stored, these systems may generate inaccurate or unreliable information.